Results for “pmapper”

Design and ship production-ready MCP servers from OpenAPI contracts instead of hand-written tool wrappers. Supports Python and TypeScript, schema validation, and safe evolution.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Generate a complete PHP Model Context Protocol server project with tools, resources, prompts, and tests using the official PHP SDK.

Official Puppeteer Model Context Protocol Server for browser automation.

Programmatic canvas toolkit for creating, editing, and refining Excalidraw diagrams via MCP tools with real-time canvas sync. Use when an agent needs to (1) draw or lay out diagrams on a live canvas, (2) iteratively refine diagrams using describe_scene and get_canvas_screenshot to see its own work, (3) export/import .excalidraw files or PNG/SVG images, (4) save/restore canvas snapshots, (5) convert Mermaid to Excalidraw, or (6) perform element-level CRUD, alignment, distribution, grouping, duplication, and locking. Requires a running canvas server (EXPRESS_SERVER_URL, default http://localhost:3000).

Scaffold and implement Model Context Protocol (MCP) servers that expose external services, APIs, and data sources as typed tools and resources for LLM agents. Use when the user says "build an MCP server", "give Claude access to X", "create an MCP tool", "expose my API to an agent", or "AI agent integration".

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Guides the creation of high-quality MCP servers that let LLMs interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Conducts autonomous multi-source research using a planner/executor architecture and an MCP server, with tools for deep research, quick search, report writing, and source retrieval.

MCP client: connect servers, register tools (stdio/HTTP).

Drive Palmier Pro, an open source AI-native macOS video editor (Swift, SwiftUI/AppKit, AVFoundation) that exposes its timeline as an MCP server at `http://127.0.0.1:19789/mcp` so Claude Code/Desktop, Cursor, or Codex can read and edit a project's tracks, clips, media, transcript, captions, color/effects, and trigger generative AI (video/image/audio) requests side-by-side with a human editor. Use when the user wants to connect an agent to Palmier Pro's MCP server, call its timeline/clip/media/generation tools (`get_timeline`, `add_clips`, `move_clips`, `generate_video`, ...), build/run/test the Swift app from source, or debug the MCP tool surface in `ToolDefinitions.swift`/`ToolExecutor+*.swift`. Triggers on: "palmier pro", "palmier-pro", "AI video editor MCP", "connect Claude to my video editor", "palmier MCP server", "edit my timeline with an agent", "swift build PalmierPro", "palmier-pro mcpb", "manage_project"/"get_timeline"/"add_clips" tool.

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.

The current Pencil MCP tool surface, `execute` idiom catalog, transport table, and document-discipline rules. Read before any Pencil call, in either `cli-app` or `editor` mode.

Guides the creation of high-quality MCP servers that let LLMs interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

MCP Server Builder

MCP client: connect servers, register tools (stdio/HTTP).

MCP Server Builder

Expert MCP (Model Context Protocol) server developer creating safe, performant, production-ready servers with proper security, error handling, and developer experience. Activate on 'create MCP', 'MCP server', 'build MCP', 'custom tool server', 'MCP development', 'Model Context Protocol'. NOT for using existing MCPs (just invoke them), general API development (use backend-architect), or skills/agents without external state (use skill-coach/agent-creator).

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for integrating external services with LLMs.

Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver via Python, C/C++, or CLI interfaces.

Complete MCP ecosystem — native client configuration, mcporter CLI, ACP subagent integration (Cursor, Claude Code), MCP server mode, and application-specific MCP integrations.